The Review of Economics and Statistics
VOL. XC NUMBER 4NOVEMBER 2008
MARKET DISTORTIONS WHEN AGENTS ARE BETTER INFORMED: THE
VALUE OF INFORMATION IN REAL ESTATE TRANSACTIONS
Steven D. Levitt and Chad Syverson*
Abstract—Agents are often better informed than the clients who hire them
and may exploit this informational advantage. Real estate agents have an
incentive to convince clients to sell their houses too cheaply and too
quickly. We test these predictions by comparing home sales in which real
estate agents are hired to when an agent sells his own home. Consistent
with the theory, we find homes owned by real estate agents sell for 3.7%
more than other houses and stay on the market 9.5 days longer, controlling
for observables. Greater information asymmetry leads to larger distor-
tions.
I. Introduction
BECAUSE of specialization, individuals rely heavily on
the advice of experts in making decisions. For activities
as varied as medical treatment, automobile repair, legal
matters, planning for retirement, or selling a home (to name
just a few), there are experts with particular skills, knowledge,
and experience willing to provide their services.
A defining characteristic of transactions involving the
hiring of an expert is the informational advantage enjoyed
by the expert relative to the client seeking advice. As a result
of this private information, expert agents may mislead their
clients by exaggerating the costs or difficulty of a solution,
providing unneeded services, or otherwise distorting the
information to maximize the expert’s own payoff. For example,
a lawyer may argue that his services are necessary in
preparing a simple will, even though the lawyer would use
the same off-the-shelf software a client could buy. An auto
mechanic may suggest overhauling the entire engine when
only a small part needs replacing. A travel agent may only
mention flights on airlines that pay the highest commission
rates to travel agents for booking.
In this paper, we focus on the relationship between a real
estate agent and a home seller. The real estate agent is likely
better informed about the value of the house and the state of
the local housing market than is the seller. (More formally,
this information advantage might be thought of as the
agent’s having a more accurate signal of the distribution of
likely offers on the house.) Typical residential real estate
contracts have the real estate agent receiving only a small
fraction of the purchase price of a home, but bearing much
of the cost of selling the house (for example, showing the
home to prospective buyers, hosting open houses, and often
advertising and marketing expenditures). This induces a
misalignment of incentives between the seller and agent.1
The agent has strong incentives to sell a house quickly, even
at a substantially lower price, and thus may encourage
clients to accept suboptimally low offers too quickly. A
rational homeowner will take the agent’s distorted incentives
into account and, as in Crawford and Sobel (1982),
discount appropriately the agent’s advice regarding whether
a particular offer should be accepted or rejected. Nonetheless,
as we formally derived in an earlier version of this
paper (Levin & Syverson, 2002), if the agent is sufficiently
better informed, the agent’s optimal strategy is to advise the
homeowner to accept any offer that is in the best interest of
the agent to accept, and the homeowner’s actions may be
influenced by the agent’s advice.2 It is rational for the
homeowner to sometimes follow this advice, despite being
fully cognizant of the agent’s desire to mislead, because a
home seller’s and an agent’s incentives are well-enough
aligned on net so that the seller can benefit from the agent’s
superior information in some instances, at the cost of the
agent’s distorting information in a way that hurts the home
Received for publication February 3, 2006. Revision accepted for
publication August 7, 2007.
* University of Chicago, NBER, and American Bar Foundation; University
of Chicago and NBER.
We would like to thank Gary Becker, David Card, Vincent Crawford,
Roland Fryer, Edward Glaeser, Michael Greenstone, Jens Ludwig, Chris
Mayer, Tobias Moskowitz, Kevin Murphy, Derek Neal, Franc¸ois OrtaloMagne´,
Canice Prendergast, Phil Reny, Andrei Shleifer, three anonymous
referees, and the editor Daron Acemoglu for helpful discussions and
advice. Elias Bruegmann and Marina Niessner provided truly outstanding
research assistance. Thomas Fumo aided us in obtaining the data. Financial
support was provided by the National Science Foundation. Correspondence
can be addressed to either of the authors at Department of
Economics, University of Chicago, 1126 E. 59th Street, Chicago, IL
60637.
1 An interesting question to which we turn in section VI is why existing
contractual arrangements persist in light of this distortion.
2 This older version is available at http://home.uchicago.edu/ϳsyverson.
Note that in the formal model used in the earlier version, the agent cannot
credibly convey any information beyond a simple recommendation to
either accept or reject the offer. Any message that attempted to further
signal the intensity of the agent’s preferences is cheap talk. For any offer
that is high enough that the agent would like the seller to accept, the agent
will have the incentive to falsely portray the offer as being extremely high,
even if it is just above the acceptability cutoff. As a consequence, the seller
will ignore such information (Crawford & Sobel, 1982). If we were to
allow cash transfers from the agent to the seller, this would provide the
agent a way to convey more information about the attractiveness of the
offer. When a really good offer arrives, the agent could credibly signal this
by offering to make an additional payment to the seller if the offer is
accepted.
The Review of Economics and Statistics, November 2008, 90(4): 599–611
© 2008 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology
seller in other instances.3 Thus, the primary predictions of
the theory are that (i) for two identical houses, one owned
by a real estate agent and the other owned by a client of the
real estate agent, the real estate agent’s home will stay on
the market for a longer period of time and sell for a higher
price, and (ii) the greater the informational advantage of the
real estate agent, the larger these two differences will be.
The extent of the distortion induced by misaligned agent
incentives may be considerable. Real estate agents typically
bear a substantial fraction of the marketing costs involved
with a home sale: advertising, accompanying potential buyers
on visits to the home, conducting open houses, and
negotiating offers. Typically, however, the agent receives
only a small percentage of each marginal dollar of the price
for which the house sells. (In the United States, this is
usually about 1.5%: a total commission of 6% of the sales
price is split evenly between the buyer’s and seller’s agents,
both of whom then give half to their firms, leaving 1.5% for
each agent.) If the combined financial and opportunity cost
to an agent of selling a house were $200 per week, then an
agent earning 1.5% on the margin would be indifferent
between selling the house today and waiting one more week
and receiving an offer $13,333 higher with certainty. The
homeowner, on the other hand, would much prefer to wait
a week and take the higher offer. On a $300,000 house, the
homeowner’s one-week return from waiting would be over
4%.4
Real estate transactions provide an unusually attractive
setting to test the impact of information distortion by experts.
Unlike many experts (such as surgeons), real estate
agents not only provide their services to clients, but also sell
their own homes. When a real estate agent sells his own
home, he is a residual claimant on the full surplus from the
sale and thus has optimal incentives. By comparing sale
prices and time on the market for homes where the agent is
hired by a client versus when the agent sells his or her own
home (and controlling for other factors), we have a simple
test of the distortions induced by the private information on
the part of agents.
Using a data set of nearly 100,000 home sales, of which
roughly 3,300 are agent-owned, we find that, even after
controlling for a wide array of house and neighborhood
characteristics, agent-owned homes sell for about 3.7% (or
roughly $7,600 at the median sales price) more than comparable
houses and stay on the market an extra 9.5 days
(about 10%) longer, even after controlling for a wide array
of house and neighborhood characteristics. Although a price
difference of $7,600 is large for the consumers, the real
estate agent’s personal share of that sum is only $114. It
does not seem unreasonable that a self-interested agent
would be willing to forego $114 to avoid having a client’s
home on the market an additional ten days.
This basic result is consistent with information distortion
on the part of agents, but also with other competing hypotheses.
For instance, real estate agents own homes that systematically
differ from those of their clients in ways that
make them sell slowly, but at high prices. While it is always
difficult to rule out stories about unobservables, we present
below a number of arguments against such an explanation.
Two other explanations for the results are that real estate
agents have lower discount rates than their clients or are less
risk averse.5 The gap we observe between agent-owned and
client-owned homes, however, is too large to be explained
by either divergent discount rates or differences in risk
aversion. The implied annual interest rate associated with
waiting an additional 9.5 days to obtain a 3.7% higher price
is 140%. It is difficult to believe that even relatively impatient
homeowners would willingly forego such a high expected
return. Further, as we detail below, even a conservative
estimate of the coefficient of relative risk aversion
necessary to make a seller indifferent between a certain
current offer and a risky future offer implies levels of risk
aversion generally considered hard to reconcile with observed
choices.
A final alternative model that could plausibly generate
our empirical findings is one where shirking by the agent
leads offers to be drawn at a lower rate or from a worse
distribution than if the agent were selling the identical home
for himself. However, theoretical results from the job search
literature suggest for many common distributional forms,
agent shirking that lowers offer arrival rates or results in a
poorer distribution of offers would increase the expected
time until a seller accepts an offer. This is inconsistent with
the fact that we see non-agent-owners selling sooner than
agents.
Beyond our basic result, we find additional evidence that
is consistent with an agent information distortion explanation,
but would not be predicted by these alternative hypotheses.
Namely, the gap between agent-owned homes and
client homes is largest in instances where agents are likely
3 Since Crawford and Sobel (1982), a rich theoretical literature has
developed that analyzes strategic information transmission. Theory that is
particularly relevant to our analysis includes recent work on the role of
experts by Taylor (1995), Krishna and Morgan (2001), Levy (2004), and
Fong (2005). For surveys of the broader literature, see Kennan and Wilson
(1991) and Riley (2001). There is also a great deal of work addressing how
contracts are structured to encourage agents to exert the optimal amount
of effort (for example, Grossman & Hart, 1983; Sappington, 1991).
Prendergast (2002) argues that the empirical evidence supporting the
hypothesized tradeoff between risk and effort is tenuous.
4 Another way of putting into perspective just how distorted are agents’
incentives is to compare real estate to sharecropping, where some have
argued that the contractual form leads to important distortions (for example,
Shaban, 1987). In the typical sharecropping arrangement, output is
split 50-50 between the land owner and the sharecropper; in real estate, the
seller’s agent receives a much smaller share of the marginal profit.
5 While we are unaware of any empirical evidence documenting risk
tolerance of agents, the fact that real estate agents have chosen a profession
with a highly variable income stream argues in favor of greater risk
tolerance on the part of agents. On the other hand, shocks to home values
in a market not only affect the price an agent receives for his own home,
but also affect the agent’s income stream (both because agent commissions
are tied to sale prices and because the volume of home sales tends
to fall as prices fall).
THE REVIEW OF ECONOMICS AND STATISTICS600
to enjoy the greatest informational advantage. We examine
such differences along three dimensions. First, we stratify
the sample as a function of how heterogeneous the housing
stock is on a particular city block. Greater heterogeneity is
likely to increase the agent’s informational advantage by
reducing the availability to the non-agent homeowner of
directly comparable prior home sales. We find that on
houses on blocks in the upper third of our sample in terms
of heterogeneity, agents get 4.3% more for their houses and
stay on the market an extra 9.5 days. In contrast, in the third
of the sample where houses on the block are most alike,
agents obtain only 2.3% more for their houses and time on
the market is not significantly different. Second, the rise of
the Internet has made it much easier for sellers to directly
observe the characteristics of other houses on the market
and to find recent transaction prices, reducing the informational
advantage of realtors. Consistent with the theory
(although perhaps not entirely convincing because of its
reliance on time series variation), in the period 1992–1995,
observationally equivalent agent homes sold for 4.9% more
than those of their clients and stayed on the market over two
weeks longer. From 2000 to 2002, in contrast, agents obtained
only 2.9% more for their houses, which stayed on the
market 2.5 days longer. Finally, in the case where the buyer
is not represented by a realtor, the seller’s realtor may have
a greater ability to determine the sale price. We find that the
absence of a buyer’s agent has no impact on the sale price
of client homes, but is associated with a 1.7% higher sale
price when the realtor sells his own house. This finding
suggests that the realtor successfully exploits uninformed
buyers when selling his or her own house, but does not reap
the available gain for clients (perhaps because tough bargaining
might jeopardize a deal being reached).
Our paper builds on a small empirical literature in this
area. Hubbard (1998, 2002) analyzes data from the California
vehicle inspection emission market and finds that inspection
suppliers tend to let vehicles pass inspections, even
though they would incur a short-run benefit from repairing
those that fail—the reason being that lenient inspections are
rewarded with repeat business. Gruber and Owings (1996)
provide tantalizing evidence of how physicians may distort
decisions to further their own interests. Doctors in areas
with declining birth rates are found to be much more likely
to perform caesarean sections than are doctors in growing
areas. Gruber and Owings interpret this result as possible
evidence that excess capacity leads doctors to induce demand
for more expensive services from their clients. The
research most similar to ours is Rutherford, Springer, and
Yavas (2005), who, in independent work, find that agentowners
earn higher selling prices on their homes, but see no
time-on-market difference. Their focus is on the effort
exerted by the agent, rather than information distortion.
Consequently, they do not test, as we do here, whether
agent-owners do systematically better in market subsets
where one might expect they hold a greater information
advantage.6
The structure of the paper is as follows. Section II
introduces the data used. Section III presents the basic
empirical analysis. Section IV discusses the competing
theoretical explanations and undertakes additional analysis
of the data in an attempt between the alternatives. Section V
discusses the results and Section VI concludes.
II. Data Used in the Analysis
The data we use encompass nearly 100,000 home sales in
suburban Cook County, Illinois (the county contains the city
of Chicago). The source of the data is the Multiple Listing
Service of Northern Illinois (MLSNI), the clearinghouse
through which realtors in the Chicago metropolitan area
notify other realtors (and, more recently, the public) of
properties for sale.7 These data have numerous strengths.
First, they cover virtually every house put up for sale in
which a seller’s agent is hired, regardless of whether the
house is eventually sold. Most of the analysis presented
below focuses on homes that actually sell, but we are also
able to explore whether listed homes sell as a check on
possible selection biases. Second, the data contain extremely
detailed information about every house on the
market, including the address, a wide range of housing
characteristics, the list price and sale price of the home, a
written description of the house’s attributes used by the real
estate agent in marketing the house, and the key dates
regarding the home sale (such as when the house goes on
the market or the date a contract is signed with a buyer).
Third, the data report whether the real estate agent has an
ownership interest in the house, which is critical to our
identification strategy.
The MLSNI data do, however, have a number of important
flaws and limitations. First, the data set does not
provide any information about homes that are for sale by
owner (FSBO). Second, the information in the database is
entered by the real estate agents themselves. There is no
independent check on the accuracy of the description of the
home’s attributes.8 Also, there are few restrictions on what
agents can type into a field in the database and no requirement
that all fields be completed. As a consequence, there
are substantial amounts of missing data for some variables
6 Germaise and Moskowitz (2004) analyze a different type of information
asymmetry in commercial real estate transactions. Our results also
contribute to growing literature on time on the market (for example,
Genesove & Mayer, 1997; Taylor, 1999; Merlo & Ortalo-Magne´, 2004)
and the impact of agency form on sales outcomes (for example, Munneke
& Yavas, 2001; Hendel, Nevo, & Ortalo-Magne´, 2007; Bernheim & Meer,
2008; and Levitt & Syverson, 2008).
7 Further details about MLSNI can be found at http://www.mlsni.com.
The archived data we utilize in this paper are not publicly available, but
can be obtained by any licensed realtor who is part of MLSNI.
8 For some entries such as listing prices and addresses, there are strong
incentives for the agent to enter the correct numbers, since this database
is the primary mechanism through which other agents learn what properties
are currently for sale.
MARKET DISTORTIONS WHEN AGENTS ARE BETTER INFORMED 601
(such as approximate square footage), some evidence of
obvious errors, and a lack of uniformity in the way fields are
coded.9
Our primary sample is made up of single-family homes
that were listed for sale in 34 Cook County suburbs during
the period 1992–2002. These suburbs are the 34 largest
municipalities in the county in which the majority of properties
listed for sale are detached single-family homes (this
excludes the city of Chicago, for which a notably smaller
share of real estate transactions are detached single-family
homes) and in which sales of newly constructed homes
represent a small fraction of overall sales.
We impose a number of restrictions on the data. We use
county tax identification numbers to link listing and sales
activity on a given property over time. Properties that are
missing tax identification numbers, or have errors in these
numbers (for instance, two properties listing the same tax ID
number but located in different cities) are dropped from the
sample. The ability to link separate listing episodes for a
given house is important when we measure time-to-sale,
because houses are sometimes strategically de-listed and
quickly re-listed in order to reset the “days on market” field
in the MLS listing. We therefore compute time-to-sale by
summing across all of a house’s listing periods that are
separated by fewer than 180 days. Additionally, we drop
from the sample any home that is sold twice within a
three-year period. This is due to concerns that the house has
been purchased and rehabbed for resale. In the presence of
imperfect measures of a house’s quality, these repeat transactions
may yield particularly misleading results, especially
if real estate agents are more likely to be rehabbers than
other sellers.10
After these initial exclusions, we are left with listings for just
under 127,000 homes. About 22% of these homes are withdrawn
from the market before a sale ever occurs, and hence are
not used in our sample. (This raises sample selection issues that
will be discussed below.) We also eliminate a small number of
properties with either very low or high listing prices or sale
prices (less than $50,000, more than $3,000,000, or a price
more than five times the median value in that city), and
properties with suspicious entries such as those reporting no
bathrooms, no kitchen, and so on. Finally, in order to estimate
all models with a common sample, about 450 listings are
removed because of missing data for one or more of the
variables included in the hedonic model. This leaves us a final
data set with roughly 98,000 home sales. About 3.4% of the
observations in our data—3,330 sales—involve a real estate
agent selling his or her own home.
Table 1 presents selected summary statistics for the key
variables in the data set.11 The first two columns report the
mean and standard deviation of these variables over the
entire sample. Columns 3–5 show a set of three statistics
regarding data variations within city-year cells. Column 3
contains the standard deviations of the variables’ withincity-year
components; that is, the standard deviation of the
residual when the variable is regressed on a set of city-year
dummies. Column 4 shows differences between the means
of the agent-owned and non-agent-owned home sales within
each city-year, and column 5 reports the p-value of a test
for equality of the means. Columns 6–8 report data that
9 For instance, we observe over fifty different entries for the field asking
the presence or absence of “air conditioning,” corresponding to different
conventions for abbreviating responses and different types of systems
such as wall units, central air, zoned central air, space-pacs, or a combination
thereof.
10 A rehabber can get better access to properties and can collect commissions
on property transactions by becoming a real estate agent. Thus
most rehabbers are likely to also have real estate licenses. It is worth
noting, however, that our empirical results are not sensitive to excluding
repeat sales.
11 The full set of summary statistics for all variables in the empirical
specifications can be found in Levitt and Syverson (2005).
TABLE 1.—SUMMARY STATISTICS
Variable
(1) (2) (3) (4) (5) (6) (7) (8)
Full Sample Within City and Year: Within Block:
Mean
Standard
Deviation
Standard
Deviation
Mean Agent-Owned
Minus Mean
Non-Agent-Owned
p-Value of
Equality in
Column 4
Standard
Deviation
Mean Agent-Owned
Minus Mean
Non-Agent-Owned
p-Value of
Equality in
Column 7
Outcome variables
Sale price, if the home sells 271,405 216,797 151,330 57,591 Ͻ0.01 108,960 48,445 Ͻ0.01
Sale price (logged), if the home sells 12.33 0.577 0.335 0.128 Ͻ0.01 0.246 0.117 Ͻ0.01
Days to sale, if the home sells 93.62 118.6 113.3 24.18 Ͻ0.01 97.18 14.83 Ͻ0.01
Whether the home ever sells 0.78 0.42 0.41 Ϫ0.07 Ͻ0.01 0.36 Ϫ0.04 Ͻ0.01
Basic housing characteristics
Number of bedrooms 3.315 0.84 0.788 0.168 Ͻ0.01 0.587 0.11 Ͻ0.01
Number of baths 1.818 1.295 1.242 0.196 Ͻ0.01 1.108 0.14 Ͻ0.01
Number of other rooms 4.041 1.166 1.082 0.246 Ͻ0.01 0.832 0.181 Ͻ0.01
Capacity of garage in cars 1.69 0.655 0.601 0.065 Ͻ0.01 0.504 0.075 Ͻ0.01
Notes: Summary statistics reflect our sample of roughly 98,038 single-family home sales over the period 1992–2002 in 34 Cook County, Illinois, suburbs. Homes sold without real estate agents are excluded from
the sample, as are homes that are listed on the Multiple Listing Service but do not sell, and Multiple Listing Service entries with data errors or inconsistencies. All variables included in the data are self-reported
by the real estate agent listing a home for sale. Columns 1 and 2 report information for raw data covering the whole sample. Columns 3–5 present information after removing city fixed effects and year fixed effects.
Columns 6–8 remove year fixed effects and city-block fixed effects. The basic housing characteristics listed represent a small subset of the control variables used in the analysis; see the appendix for a full listing.
THE REVIEW OF ECONOMICS AND STATISTICS602
parallels the information in columns 3–5, but present withinblock,
rather than city-year comparisons.
The top panel of the table shows our primary variables of
interest: sale price (in levels and logs), time on the market,
and whether a listed home ever sells. Consistent with the
prediction of the model, even on a given block, agent-asseller
homes sell for substantially more ($48,445 on average,
or 18% of the mean sales price)12 and conditional on a
sale occurring, remain on the market for almost fifteen days
longer than other homes. We also find that a smaller fraction
of the agent-owned homes that are put on the market
eventually sell.13 This reinforces the fact that agent-owned
homes take longer to sell. If one computes the average
number of days on the market per home sale, including
properties that never sell in the numerator, agent-owned
houses are on the market 135 days per sale, compared with
only 111 for non-agent-owned houses, or 22% longer.
It is clear from the remainder of the table, however, that
the attributes and locations of homes owned by agents are
systematically different from the non-agent-owned homes.
Real estate agents tend to live in homes that are larger, even
compared with other houses on the same city block. Further
results shown in Levitt and Syverson (2005) indicate that
agent-owned homes are also newer and have greater numbers
of amenities like master baths and fireplaces. These
systematic differences in housing characteristics highlight
the importance of controls in the analysis we carry out.
Although we have an extremely rich set of covariates, the
possibility remains that realtor-owned properties might be
correlated with unobservable characteristics of the housing.
III. Core Empirical Estimates
The empirical specification we estimate to predict sale
prices and time-on-market that is most fully saturated takes
the following form:
yhtc ϭ ␤*AGENT_OWNEDht ϩ Xht␥ ϩ ␬ct ϩ ␭b ϩ εht,
where h, t, c, and b correspond respectively to house, year,
city, and city block. The dependent variable y is either a
house’s logged sales price or days on the market. The full
set of housing characteristics included in Xht is listed in the
appendix. These include categorical measures of numbers of
rooms of different types, numbers of garage stalls and
fireplaces, dummies for the presence of certain amenities
like master baths, 21 indicator variables corresponding to
the style of the house (for example, bungalow, Cape Cod.
colonial, tudor), five indicators describing the home’s exterior
(such as brick, wood, vinyl siding), and nearly 100
indicators for keywords included in the written description
of the home (such as spacious, amazing, granite, youthful).
In addition, we allow for city-specific variation in annual
prices (␬ct), and fixed effects for each city block (␭b).
Table 2 presents a series of regression results in which the
set of control variables is expanded as one moves from left
to right. The logged sales price coefficients on agent-owned
houses are presented in the top row of numbers, and the
corresponding coefficients for days on the market are shown
in a lower row. Heteroskedasticity-consistent standard errors
are in parentheses. In addition, the bottom row of the
table reports the “excess returns” accruing to the realtor,
computed as the additional sale price they receive minus the
cost of waiting longer to sell. We use an annual discount rate
of 20% in these calculations—a number that is high, but
consistent with the previous research of Genesove and
Mayer (1997).
Column 1 includes fixed effects for each city-year pair as
well as controls for the subset of our observed housing
characteristics that reflect the scale of the house (for example,
number of bedrooms, number of bathrooms, how many
cars the garage can hold). Controlling for house scale
substantially reduces the estimated impact of agent ownership
from the simple within-city mean differences in table 1.
The sales price difference between agent-owned and nonagent-owned
homes is 4.8%, almost two-thirds less than the
within-city difference obtained before conditioning on
scale. Thus the fact that agents live in their cities’ largerthan-average
homes, as seen before in table 1, is in part
responsible for the observed differences in sales prices and
time on the market. The difference in time on the market is
about seventeen days, one-third less than the unconditional
value in table 1. Adjusting for the longer time on the market,
realtors achieve an extra return of 3.9% on their houses. The
coefficients on the control variables (not shown) indicate
that as expected, more is better.
A particular concern is testing the hypotheses of the
model is that agents live in houses that are especially
attractive along dimensions that are difficult for us to
observe or quantify. For instance, agents may have good
taste, take better care of their houses, or live in houses that
have more features desired by potential home buyers (master
bedroom suites, modern kitchens, and so on). The
specifications in columns 2 and 3 of table 2 attempt to
control for possible differences in quality in two ways.
12 The skewness of the house price distribution may exaggerate the size
of a typical difference. The mean difference in logged sales prices—the
primary difference we focus on below—suggests a 12% difference in sales
prices.
13 Agent-owned homes that never sell remain on the market an average
of 182 days, compared with 176 days for non-agent-owned homes. The
results that we present in our tables are restricted to properties that actually
sell, raising sample selection issues. We do not model the delisting
decision; if agent-owners make systematically different decisions about
pulling their houses off the market, some of the differences we find among
houses that are sold may reflect delisting behavior. If agents with idiosyncratically
lower-quality houses are more likely to delist than non-agent
owners, for example, this could explain our finding below that agentowners
receive higher sales prices. We cannot completely rule out such
selection effects on price, but we are able to address the time-on-market
differences. We have estimated a hazard model on the probability a
particular home sells as a function of observables. This allows us to
directly account for the fact that certain listing episodes are truncated (that
is, pulled off the market) before a sale is ever made. We found, reassuringly,
that the hazard model results are close to the simpler OLS specifications.
MARKET DISTORTIONS WHEN AGENTS ARE BETTER INFORMED 603
In column 2, a wide range of housing characteristics are
added, such as variables for the presence of a master bathroom,
the number of fireplaces, the presence of air conditioning, the
house’s age (category), the exterior material (brick, stucco,
etc.), and the house’s architectural style (colonial, bungalow,
etc.). We also control for the listing agent’s total number of
sales as a proxy for reputation and experience effects. When
these controls are added, the agent-owned coefficient in the
sale price equation drops slightly, from 0.048 to 0.042, while
the estimated difference in days on the market falls from 16.89
to 11.03. Because the gap in time on the market shrinks
between column 1 and column 2, the implied excess return
accruing to agent-owned homes falls only half as much as the
sales price gap (that is, from 3.9% to 3.6%).
In column 3, we add dummy variables for a large set of
keywords and phrases used in the written marketing description
of the house. When these descriptors are added,
neither coefficient changes by more than 10%. This is
despite the fact that many descriptors enter into the regression
significantly. Systematic quality differences appear to
be responsible for part of the gap between agent-owned and
non-agent-owned homes’ sales prices and times-on-market,
although most of the difference is explained by broad
indicators of age and style rather than by more subtle
characteristics picked up by the agents’ descriptions.
A way to further remove possible biases due to unobservables
is to include block fixed effects so that identification
of the parameters comes from a comparison of sale prices of
different homes on the same block, rather than from homes
in different areas of a city. Homes on the same block are
nearly identical in terms of school quality, crime, proximity
to public transportation and parks, and so on. As demonstrated
in column 4, the R-squared of the regressions—and
particularly those for time-on-market—jump noticeably
when block effects are added, suggesting the presence of
important differences across blocks within a city. The estimated
sales price and time-on-market impacts of a home
being agent-owned, however, see only small and statistically
insignificant drops with the inclusion of block fixed
effects. The implied price gap between agent-owned and
non-agent-owned home sales is 3.7%, with agent-owned
homes staying on the market 9.5 days longer.14
The results of table 2 suggest that the primary dimension
along which agent-owned houses differ from other homes is in
terms of scale and readily identifiable amenities such as master
baths. Controlling for these basic factors dramatically lowers
the gaps between agent-owned and other houses, relative to the
means of the raw data. Including a wide range of controls in
addition to these basic ones does account for much of the
residual variance in sale prices, but has a relatively small
impact on the measured impact of agent-ownership.
The estimated realtor-owned-home coefficient has a plausible
magnitude. A 3.7% divergence in sales price for the
14 If we push the data even further, adding block-year interactions so that we
only identify the coefficients off of variation across multiple homes on the
same block that have an original listing date in the same calendar year, we find
that agent-owned homes sell for 3.5% more (standard error of 0.5%) and stay
on the market 11.2 days longer (standard error of 3.7 days). Forty percent of
the homes in our sample are on blocks with multiple sales in that year.
Merlo and Ortalo-Magne´ (2004) find that lower list prices trade off the
possibility of selling at a higher price for a higher arrival rate of potential
buyers. When we reestimate the specification of column 4 using original
listing prices rather than sales prices, we find list prices of agent-owned
homes are 3.4% (s.e. ϭ 0.3) higher than those of comparable non-agentowned
homes.
Since real estate agents tend to own homes that are better than the
average house on a block, we have also estimated specifications limiting
the sample to the 20% of houses on the block that have the highest
predicted sale price based on our hedonic regression. The results are very
similar to those for the whole sample.
TABLE 2.—THE IMPACT OF AGENT-OWNERSHIP STATUS ON SALE PRICE AND TIME-TO-SALE
(1) (2) (3) (4)
Dependent Variable: ln(Sale Price of Home)
Coefficient on agent-owned home 0.048 0.042 0.038 0.037
(Standard error) (0.004) (0.004) (0.003)
R2 0.856 0.886 0.896 0.958
Variable: Days to Sale
Coefficient on agent-owned home 16.89 11.03 10.25 9.47
(Standard error) (2.42) (2.40) (2.39) (2.25)
R2 0.123 0.130 0.139 0.384
Controls included:
City ϫ year interactions Yes Yes Yes Yes
Basic house characteristics Yes Yes Yes Yes
Indicators of house quality No Yes Yes Yes
Keywords in description No No Yes Yes
Block fixed effects No No No Yes
“Excess return” of agent
assuming a 20% annual
discount rate 0.039 0.036 0.032 0.032
Notes: Regression coefficients are reported in the table, along with standard errors in parentheses. Results are based on a sample of 98,038 single-family home sales in 34 Cook County, Illinois, suburbs over the
period 1992–2002. The dependent variable in the top panel of the table is the natural log of the sale price; the dependent variable in the bottom panel is the number of days on the market. Each coefficient reported
in the table is from a separate regression. The other variables included in each specification are noted in the table, but the coefficients on these other variables are not reported here (table 3 presents a subset of
coefficient estimates for these controls). See the appendix for a complete list. The table’s bottom row reports the implied “excess return” accruing to agents selling their own homes, computed as the additional price
received for a home adjusted for the extra time on the market, under the assumption of a 20% annual discount rate.
THE REVIEW OF ECONOMICS AND STATISTICS604
median-priced non-agent-owned home in our sample
($206,000) implies a $7,600 higher sales price for an equivalent
agent-owned home. The additional time on the market
spent by agent-owned homes is roughly 10% of the average
listing time. Even if one uses an extremely high annual
discount rate of 20% (but one that is consistent with the
behavior of home sellers in Genesove & Mayer, 1997), then
adjusting for the longer time on the market, agent houses
sell for the equivalent of 3.2% more than the homes of their
clients, as shown in the bottom row of table 2.15
Table 3 reports the parameters for a subset of the control
variables included in the regression with block fixed effects.16
While these are not the primary focus of our analysis, it is
reassuring that the coefficients on these characteristics are
sensible. For example, all else constant, a house with four
bedrooms sells for 6.1% more than one with three bedrooms,
going from 1.5 to 2.0 baths is worth 1.4%, central air conditioning
is worth 6.8% relative to no air conditioning, brick
houses sell for 3.6% premium over those with siding, and
colonials sell for 8.5% more than otherwise identical ranchstyle
homes.15 Indeed, our results may provide a partial explanation for Genesove and
Mayer’s (1997) finding that home sellers in their sample have a high implied
discount rate. If our analysis is correct, sellers sell too quickly not only
because of impatience, but also because agents convince them to do so.
16 Only a subset is shown for space reasons. Full results are available in
Levitt and Syverson (2005).
TABLE 3.—SAMPLE OF COEFFICIENT ESTIMATES ON CONTROL VARIABLES
(SPECIFICATION INCLUDING BLOCK-LEVEL FIXED EFFECTS)
Dependent Variable:
ln(Sale Price) Days to Sale
Coeff. s.e. Coeff. s.e.
Explanatory variable
Bedrooms (1 bedroom omitted):
2 bedrooms 0.177 0.013 5.02 10.77
3 bedrooms 0.264 0.013 13.85 10.76
4 bedrooms 0.325 0.013 25.10 10.81
5 bedrooms 0.378 0.013 36.64 10.95
6ϩ bedrooms 0.422 0.014 38.70 11.46
Rooms (not bed or bath, 7 or more omitted):
1 additional room Ϫ0.216 0.006 Ϫ30.09 4.60
2 additional rooms Ϫ0.196 0.005 Ϫ27.56 4.29
3 additional rooms Ϫ0.158 0.005 Ϫ22.23 4.21
4 additional rooms Ϫ0.126 0.005 Ϫ16.54 4.20
5 additional rooms Ϫ0.090 0.005 Ϫ10.76 4.29
6 additional rooms Ϫ0.054 0.006 Ϫ10.44 4.80
Bathrooms (4 or more omitted):
1.0 bath Ϫ0.384 0.005 Ϫ48.79 4.05
1.5 baths Ϫ0.333 0.005 Ϫ46.40 3.89
2.0 baths Ϫ0.319 0.005 Ϫ44.53 3.77
2.5 baths Ϫ0.234 0.004 Ϫ38.52 3.52
3.0 baths Ϫ0.239 0.005 Ϫ32.32 3.78
3.5 baths Ϫ0.115 0.004 Ϫ25.79 3.45
Style:
American four square 0.028 0.005 Ϫ0.06 3.95
Bungalow Ϫ0.030 0.003 Ϫ1.86 2.18
Cape Cod Ϫ0.015 0.003 Ϫ3.68 2.20
Colonial 0.056 0.002 4.97 1.92
Cottage Ϫ0.049 0.006 4.65 4.50
English 0.057 0.004 1.14 3.26
French provincial 0.111 0.008 14.61 6.36
Georgian 0.031 0.004 3.16 2.95
Prairie 0.089 0.006 16.16 5.13
Quad-level Ϫ0.001 0.007 Ϫ5.30 5.39
Ranch Ϫ0.029 0.002 Ϫ0.20 1.70
Trilevel Ϫ0.013 0.003 6.99 2.46
Tudor 0.066 0.006 15.05 4.49
Victorian 0.054 0.005 8.90 3.97
Siding:
Brick 0.033 0.002 10.07 1.94
Aluminum or vinyl Ϫ0.001 0.002 11.59 1.98
Wood Ϫ0.003 0.003 8.94 2.48
Stucco 0.010 0.003 3.15 2.82
Misc. quality:
Central air conditioning 0.068 0.002 0.58 1.41
Other air conditioning 0.030 0.002 4.20 1.70
Master bedroom bath 0.044 0.002 1.83 1.41
Agent sales (logged) 0.003 0.000 Ϫ2.03 0.31
Notes: This table reports a subset of the coefficients on the control variables in the specifications shown in column 4 of table 2. See the notes to table 2 and the appendix for further details.
MARKET DISTORTIONS WHEN AGENTS ARE BETTER INFORMED 605
Although not shown in tabular form, the coefficients on
the words used in the written descriptions of the properties
are also of some interest. Words that indicate obvious
problems with a house, such as “foreclosure,” “as-is,” or
“handyman special,” are associated with substantially lower
sale prices.17 Words that correspond to well-defined and
desirable attributes of the house that are not otherwise
communicated in the property listing, such as “granite,”
“maple,” or “gourmet,” are related to higher sale prices.
Among the words that have zero or even negative correlations
with prices are some that are superficially positive, but
in effect damn with faint praise (like “clean,” “quiet,” and
“youthful”); words and phrases that do not describe particular
characteristics of the house (such as “fantastic,”
“charming,” and “!”); and those that characterize features of
the house already covered by other information available in
the listing sheet (such as “spacious”).
IV. Competing Theoretical Explanations
A number of different theories can, in principle, produce
the basic pattern of results we observe in the data. In this
section, we attempt to distinguish between these competing
hypotheses. We begin with a discussion of how such results
could be generated within competitive markets without
either informational frictions or agency problems. In section
V we then turn to stories involving information distortion or
shirking on the part of agents.
There are three potential reasons why agent-owned homes
might sell for more and remain on the market longer than the
homes of clients, even if agents provide the same level of
service and advice to clients as when they sell their own
homes: unobserved differences between the characteristics of
agent-owned homes and those of their clients, greater patience
on the part of agents, and less risk aversion on the part of
agents. We consider these three explanations in turn.
A. Unobserved Differences between Agent-Owned and
Client Homes
Despite our best attempts to control for housing characteristics,
it remains a possibility that agents own homes that
systematically differ from those of non-agents on dimensions
that we cannot observe. Our results are not easily
reconciled with a model in which real estate agent houses
are more attractive on unobservable dimensions such as
good taste in choices of de´cor; if that were the case, one
would expect agent homes to sell more quickly than client
homes, rather than less quickly. If, instead, agent-owned
homes are decorated in an expensive but idiosyncratic
fashion, these houses may sell slowly because they appeal to
a small subset of buyers, but fetch a high price because
within this set of buyers the home is especially highly
valued. A wide array of anecdotal evidence, however, argues
that real estate agents advocate decorating choices that will
give their homes the broadest appeal possible.18
Nonetheless, we attempt to quantify the potential importance
of unobserved heterogeneity in two ways, neither of
which is definitive. First, following the methodology of
Murphy and Topel (1990), under the assumption that the
unexplained variation in our outcome variables is related to
our agent-owned variable in the same way as the variation
that we can explain, we can estimate the true coefficients,
even if some bias due to unobservables remains in our
specifications. Moving from column 2 to column 4 of table
2, the sale price coefficient falls from 4.2% to 3.7% as the
R-squared of the regression rises from 0.886 to 0.958.
Extending that trend over the remaining unexplained variation
yields an estimate of 3.4% on the agent-owned variable
in the sale price regression. The time on the market
results, because the R-squared is lower, are slightly more
sensitive, yielding an estimated true underlying coefficient
of 5.7 extra days on the market, compared with our estimate
of 9.5 days.
A second approach to eliminating unobserved heterogeneity
is the inclusion of agent fixed effects in the specification.
To the extent that the unobserved characteristics of an
agent’s own home are correlated with the unobserved characteristics
of that agent’s clients’ homes, this specification
will reduce bias due to unobserved heterogeneity.19 With
agent fixed effects included, the coefficient on the agentowned
variable in the sale price regression is 2.9% (standard
error of 0.3%) and in the time-on-market regression the
coefficient is 6.5 days (standard error of 2.3 days).20
Based on these two approaches, it appears that unobserved
heterogeneity may explain some small portion of our
findings, but cannot explain the overall patterns.
17 Our basic findings regarding agent-owned homes are robust to excluding
these problem properties. If we drop any property that has one of
the keywords that might indicate a property in unusual disrepair (there are
a total of 15,232 such homes in our sample), the estimate on the
agent-owner dummy in the sales price regression is 0.036 (0.003), essentially
the same as that for the full sample. The coefficient in the time-onmarket
regression is 9.20 (2.44), also very close to the full-sample result.
18 On its Web site, the National Association of Realtors (2007) suggests
preparing a home for sale by “repainting dingy, soiled or strongly colored
walls with a neutral shade of paint, such as off-white or beige. The same
neutral scheme can be applied to carpets and linoleum.” In an interview in
Realtor Magazine (Evans, 2002), a home stager—an individual hired to
prepare homes for showings—described the duties involved as, “A stager
goes in and furnishes as neutrally as possible to appeal to the widest
audience.” The large Canadian real estate company Royal LePage (2007)
offers the following tip to homeowners preparing a home for sale: “Don’t
be too unique. Keep your de´cor simple and modern with neutral wall
colours. Unusual accessories and strong wall colours will limit your pool
of buyers.”
19 Agent fixed effects will also deal with possible bias arising if the set
of agents who sell their own homes is not representative of agents as a
whole.
20 Because of computational limits, we are unable to estimate specifications
that include both agent fixed effects and block fixed effects. The
results we report with agent fixed effects therefore correspond to the
specifications in column 3 of table 2 rather than column 4.
THE REVIEW OF ECONOMICS AND STATISTICS606
B. Lower Discount Rates on the Part of Agents
If real estate agents have systematically lower discount rates
when selling their own homes than their clients, the agents will
tend to receive a higher price for an otherwise identical home,
offset by a longer time to sale, just as we observe in the data.
Real estate agents may be more patient than clients if, for
instance, agents are less likely to be making job-related moves
that are time sensitive. Agents may also appear more patient if
they suffer less disutility from maintaining their home in the
state required for home showings.
The required differences in discount rates needed to
explain our results are unrealistically large, however. A
3.7% higher sale price in return for waiting an additional 9.5
days is an expected annual return rate of 140 percent.21
While Genesove and Mayer (1997) found evidence of
unusually high implied discount rates on the part of home
sellers (20% annually), this is still just one-seventh of what
would be needed to explain our findings as being driven
solely by differences in discount rates.
C. Less Risk Aversion on the Part of Agents
If agents are less risk averse than their clients, they will
place a lower value on an offer in hand today relative to the
uncertain prospect of a higher future offer. The fact that the
income stream accruing to a real estate agent is far more
variable than that of the typical American is consistent with
greater risk tolerance on the part of agents. Arguing in the
opposite direction, however, is the fact that real estate agent
wealth is more sensitive to housing price shocks than is that
of the typical client. While housing shocks affect the housing
wealth of both agents and clients, they have an additional
impact on real estate agents, since their earnings are
also affected. Negative housing shocks impact agent earnings
through price effects, since commissions are a relatively
fixed proportion of the sale price (although free entry
into the profession dampens this effect; see Hsieh & Moretti,
2003), and perhaps even more so through quantity
effects due to the decline in the volume of sales.
To put further perspective on the degree of risk aversion
necessary to explain the agent-owner gap, consider the
following back-of-the-envelope calculation. Suppose an individual
with constant relative risk aversion utility is able to
obtain a payoff X with certainty. This could be, for example,
an offer-in-hand for a house. For this person to prefer this
payoff to one equal to kX, where k is normally distributed
with mean 1.037 and standard deviation 0.1145, he would
have to have a coefficient of relative risk aversion greater
than 5.5.22 A risk aversion coefficient of 5.5 is at the top end
of values typically used in the literature; see the discussion
in Kocherlakota (1996), for example. This calculation, however,
greatly overstates the risk associated with waiting
because it assumes the seller must accept the next offer, no
matter how low. If we instead assume sellers only need to
accept offers lower than X with 50% probability (say because
some exogenous factor forces a sale), then a seller
must have a coefficient of risk aversion above 9.8 to prefer
X with certainty.
V. Distinguishing between Shirking and Information
Distortion on the Part of Agents
To the extent that the explanations discussed above do not
appear sufficient to explain the magnitude of our findings,
there appears to be room for the role of agent distortions, via
either shirking on effort or exploiting an informational
advantage.
There are a number of pieces of evidence that persuade us
that informational asymmetries are more likely to explain
the results than shirking. From a theoretical perspective,
shirking is unlikely to lead clients to pull their houses off the
market more quickly. Agent shirking can affect offers in two
ways: by reducing the rate at which offers arrive, or by
generating offers from a lower price distribution. Burdett
and Ondrich (1985) show in a labor market setting that if the
offer distribution is logconcave (that is, the log of the
density function is concave; many standard distributions
exhibit this property, including for example the uniform,
normal, beta, exponential, and extreme value distributions),
then lower offer arrival rates, a lower mean of the offer
distribution (holding variance constant), or lower offer variance
(holding the mean constant)—each possible consequences
of agent shirking—imply longer expected times
until an offer is accepted. These predictions contrast with
our findings that non-agent owners sell sooner than do
agents.
Second, for shirking to be important it must be either
difficult for clients to observe their agent’s effort, or impossible
to verify, so that contracts cannot be conditioned on
this information. Many of the tasks performed by an agent,
however, are readily observed by the homeowner (such as
placing advertisements, conducting open houses and showings,
and generating a written description of the home’s
attributes).
Third, there is some evidence that the gap between
agent-owned homes and those of their clients systematically
vary with the agent’s informational advantage. We examine
this issue along three different dimensions, reporting the
results in table 4.
The first dimension we analyze is the degree of heterogeneity
in the housing stock on the city block where the
home is located. In areas with nearly identical homes,
sellers can learn much about their own homes’ values
simply by noting nearby sales prices. When housing stock in
an area is very heterogeneous, however, other sales prices
convey less information to sellers about their own homes’
21 If (1 ϩ r/365)9.5 ϭ 1.037, then r ϭ 1.399.
22 The level of X is inconsequential here because of the CRRA assumption.
The standard deviation of k was calibrated as the standard deviation
of logged house price residuals from our benchmark specification in table
2, column 4. We take this as a rough approximation of random dispersion
in the offer distribution.
MARKET DISTORTIONS WHEN AGENTS ARE BETTER INFORMED 607
values. We proxy heterogeneity by constructing a Herfindahl
index of home styles among houses sold on the block
in our sample period. (That is, we sum the squared shares of
each housing style on the block.) In order to be included in
the analysis, we require at least three homes to be sold on
the block. Houses are classified into 21 different styles (such
as ranch, colonial, American four square, prairie, contemporary)
using the MLS listing descriptions. We then divide
blocks into three equally sized groups according to the
Herfindahl measure. The estimates of the impact of an
agent-owned home are reported in the panel A of table 4. We
also report the implied “excess return” of the realtor as
above, where we adjust the agent-owner price gap for
differences in time on the market using an annual discount
rate of 20%.
As can be seen in the table, the sales price difference
between agent-owned homes and other homes is indeed
highest on blocks where the houses are most different. Here,
the price difference is 4.3%. The gap is smaller in the
moderate-heterogeneity blocks (3.9%), and smaller still on
the low-heterogeneity blocks (2.3%—roughly half that of
the most dissimilar blocks), all in accordance with the
notion that neighborhoods with dissimilar houses present a
larger information advantage for realtors. We can reject at
the 5% level that the coefficients from the high- and mediumheterogeneity
subsamples equal the low-heterogeneity estimate.
We cannot statistically distinguish between the former
two. The time on the market differences reflect similar
contrasts. Agents on the more heterogeneous blocks clearly
keep their houses on the market for a longer period than
non-agents, while there is no statistically significant timeto-sale
gap on those blocks with the most similar houses.
Because of imprecision in the estimates, we cannot reject
equality of the three estimated agent-owner effects across
the subsamples. Finally, the sizes of agents’ implied excess
returns in the three subsamples are in accordance with
expectations: they are largest for the most dissimilar blocks
but get progressively smaller as heterogeneity falls.23
The second dimension along which we expect to see
systematic differences in agents’ information advantage
relates to the introduction of the Internet. In recent years
information about house sale prices has become readily
available to the general public on the Chicago Tribune Web
site. In addition, sellers can now directly access a limited
version of the MLS. There are also Web-based services that
will predict the market value of a home based on econometric
models (for example, Case & Shiller, 1990) and
information the seller enters into the program. Because of
the improved information dissemination, we expect that the
information advantage of realtors has fallen over time. We
report in panel B of table 4 the results from estimating our
full hedonic specification on three subsamples of the data
stratified by time period (1992–1995, 1996–1999, and
2000–2002).
23 We have also attempted to measure heterogeneity of housing based
upon the measured heterogeneity in the overall observable characteristics
of homes sold on the block. To obtain that block-level measure of house
heterogeneity, we first regress logged sales prices on a set of city-year
dummies and block effects. The residuals from this regression are that
portion of home sales prices not driven by temporal or spatial differences
in average price levels. These residuals therefore embody between-home
differences in observable characteristics, such as the number of rooms, the
age of the home, and so on. We use the average squared residual on each
block as our measure of block heterogeneity. The results are similar to
those obtained using the Herfindahl index based on housing styles.
TABLE 4.—THE IMPACT OF AGENT OWNERSHIP BY SUBSAMPLE
(VALUES IN TABLE ARE COEFFICIENT ON AGENT-OWNED INDICATOR VARIABLE)
Subsample
Magnitude of Predicted
Agent-Owned Distortion
Dependent Variable:
ln(Sale Price)
Dependent Variable:
Days to Sale
Implied “Excess Return”
(20% Annual Discount Rate)
A. Heterogeneity of housing
stock on the block
High heterogeneity High 0.043 9.45 0.038
(0.005) (3.68)
Moderate heterogeneity Medium 0.039 11.92 0.032
(0.005) (3.82)
Low heterogeneity Low 0.023 5.09 0.020
(0.005) (4.24)
B. Time period
On the market 1992–1995 High 0.049 15.20 0.041
(0.007) (6.11)
On the market 1996–1999 Medium 0.032 7.99 0.028
(0.005) (4.14)
On the market 2000–2002 Low 0.029 2.47 0.028
(0.006) (3.98)
C. Buyer’s agent presence
Buyer’s agent absent ϫ
agent-owned home
High 0.052 N/A N/A
(0.007)
Buyer’s agent present ϫ
agent-owned home
Low 0.033 N/A N/A
(0.003)
Notes: All coefficients in the table correspond to variations on the specification reported in column 4 of table 2. Panels A and B divide the sample into mutually exclusive, exhaustive subsamples. The heterogeneity
of a city block’s housing stock is computed based on the Herfindahl index of styles of houses (such as Victorian, Georgian, or colonial) sold on the block in our sample period. Blocks with fewer than three home
sales over the course of the sample are excluded from the analysis in panel A. The remaining sample is divided into equally sized groups based on the Herfindahl measure. Panel B divides the sample according
to the year that a house is originally listed for sale. Panel C adds interactions between whether a buyer’s agent is part of the transaction and the agent-owned variable to the baseline specification.
THE REVIEW OF ECONOMICS AND STATISTICS608
As expected, the largest average sales price difference
between agent-owned and non-agent-owned homes—
4.9%—is in the earliest period. This falls by about onethird,
to 3.2%, when the Internet is starting to widely diffuse
during 1996–1999. We can reject equality of this estimate
with that from the earlier period at the 5% level. The point
estimate drops again slightly in the last period, though not
significantly (however, it is also significantly lower than the
earliest period’s estimate). The time-on-market differences
echo these patterns. Agent-owners wait more than two
weeks longer before 1996, just over a week longer in the
middle period, and two-and-a-half days longer in 2000–
2002. The time-on-market results continue to be less precisely
estimated. We we can reject equality across the
earliest and latest period at the 10% level; the two other
pairwise subsample comparisons are not significantly different.
The implied excess return for agent-owned homes,
shown in the final column, is roughly 50% higher in the first
part of the sample than in the latter parts, although indistinguishable
between the middle and end periods. There are,
of course, many other changes in the real estate market over
time (for example, price levels, availability of low downpayment
loans) besides increasing information availability
for consumers.24 Nonetheless, the time series pattern is at
least consistent with our prediction regarding information.
The third dimension along which the informational advantage
may vary is with respect to the presence of a
buyer’s agent. In most transactions, the sellers and buyers
are represented by different agents. In some transactions,
only the seller has an agent, or the same agent represents
both the seller and the buyer. There are at least two channels
through which the absence of an independent agent representing
the buyer could enhance the information advantage
of the seller’s realtor. First, the selling agent would communicate
directly with potential home buyers, providing an
additional channel through which to affect outcomes (perhaps
without the seller’s complete knowledge). If a buyer
has a realtor, on the other hand, the two realtors talk to each
other instead of the opposing clients. Second, when buyers
do not have agents, the selling realtor’s commissions double
since fees do not have to be split with a buyer’s agent.
Therefore sell-side agents have strong incentives to sell to
buyers without agents; when realtors sell their own homes,
however, the incremental gain of 1.5% from making a sale
with no buyer’s agent is only a small portion of the total
value of the house.
To test these hypotheses, we rerun our basic specification
for the sales price of the home, but add to the specification
an indicator variable for the presence of a buyer’s agent (a
value of 0 implies only the seller’s agent is involved in the
transaction), as well as an interaction of this indicator with
the agent-owned dummy.25
The results, presented in the panel C of table 4, fit nicely
with the theoretical predictions. The absence of a buyer’s
agent has a negligible impact on sale price when a selling
agent is representing a client; the logged price effect (not
reported in the table) is 0.0005 with a standard error of
0.002. When the agent is selling his or her own home, on the
other hand, the absence of a buyer’s agent is associated with
a statistically significant 1.9% increase in the sale price,
raising the agent-owned gap from 3.3% to 5.2%. One
interpretation of this result is that buyers without agents are
less well informed and susceptible to paying higher prices if
the seller’s realtor wishes to extract the surplus. But, since
the selling agent earns twice as much by selling to a buyer
without an agent, they do not attempt to extract the surplus
for their client at the risk of the deal failing to go through.
When the agent sells his or her own home, however, buyers
without agents are exploited to the full extent possible since
the agent is the residual claimant.
VI. Conclusion
Experts hold valuable information. This information is
helpful to those who hire them, but can also be a source of
welfare-reducing distortions. In this paper, we examine
economic interactions between experts and their clients in a
particular industry, residential real estate. The empirical
estimates suggest the distortions are nontrivial: agents sell
their own homes for 3.7% (roughly $7,600) more than they
sell their clients’ homes, and leave their houses on the
market roughly ten days (10%) longer. While this pattern of
results is directionally consistent with other explanations
such as differences in discount rates or risk aversion across
agents and their clients, the magnitudes of the coefficients
are not easily reconciled with such explanations. Agent
shirking on client homes also seems unlikely to be driving
our results, given that for many plausible offer distributions,
shirking would tend to cause non-agent-owned homes to
stay on the market longer than agent-owned homes. Further,
none of these otherwise plausible alternatives would explain
our findings that situations where agents are likely to have
greater information advantages are associated with larger
sales price and time-on-the-market differences between
agents and their clients. Our favored interpretation of the data
is that the combination of real estate agents’ information
24 As with many other urban areas in North America and Western
Europe, the Chicago metro area saw accelerated house price growth in the
late 1990s after several sluggish years (though the magnitudes were
relatively modest compared with some other cities—median prices in our
sample never rose more than 11.6% per year). The timing of this acceleration
raises the possibility that our results in this particular test confound
information diffusion with changes in aggregate market conditions. While
we cannot explicitly rule this out, it does not line up quantitatively with
the facts that the sales price, time-on-market, and implied information
gaps tightened most between the first and second periods, while on the
other hand, aggregate market conditions changed much more between the
second and third periods than between the first two (for example, the
median sales price grew 8.4% between the 1992–1995 and 1996–1999
periods but fully 35.3% between the second and third periods).
25 Time-on-the-market regressions will not provide meaningful information
in this context, since time on the market is a function of the entire history of
the house’s listing experience, and has little to do with precise identity of the
final purchaser and whether that person is represented by an agent.
MARKET DISTORTIONS WHEN AGENTS ARE BETTER INFORMED 609
advantage and the form of the commission received combine
to create distortions from first best. Homeowners are
induced by their agents to sell too quickly and at a price that
is too low.
Our results raise a number of important questions. First,
why do reputation concerns not more effectively discipline
agents? One reason is that repeat business with a given
client is far from certain; homeowners do not sell very
frequently and often move out of the area after doing so.
Perhaps an even more compelling reason for weak reputation
effects, however, is that homeowners do not observe the
counterfactual outcome. That is, they never see what their
house would have sold for had they waited the extra time
that agents do when selling their own homes. As List (2006)
shows, verification (being able to observe the true quality of
the good or service exchanged) is a complement to reputation;
its absence makes reputation less able to reduce the
impact of information asymmetries. In terms of empirical
patterns, when we divide the sample according to how many
homes an agent sells in total over the sample, we find no
evidence that the most prolific agents obtain higher sales
prices for their clients or sell these homes more quickly, but
these prolific agents do sell their own homes for 4.0% more
than their clients’ homes, controlling for other factors,
compared with a difference of only 2.2% for agents with
fewer listings. The difference in sales price when selling
their own homes is statistically significant at the 5% level;
there are no significant differences in time on the market for
these two groups of agents. These results are inconsistent
with reputation being an effective force in disciplining
agents. Rather, it appears that the most experienced agents
have the ability to achieve higher returns for their clients—as
evidenced by their success with their own
homes—but fail to deliver this extra value to their clients.
A second puzzle that arises is why a contractual form that
so badly misaligns agent and home-seller incentives arose
and persists. One might imagine a better alternative would
be one similar to that in the used automobile market: an
intermediary purchases used cars for resale at a markup to
buyers with higher valuations. By purchasing the used cars
outright, the intermediary then earns the full markup, rather
than a small percentage as with houses. Such a system,
however, would appear to be even less efficient than the
current setup for home sales because the carrying costs of a
home are so great. The implied interest payments on a
$200,000 home are roughly $1,000–$1,500 per month at
reasonable interest rates. If it took, say, an average of three
to six months for an intermediary to turn over a home, this
holding could well exceed the distortion we estimate under
the current system. Moreover, from a social welfare standpoint,
having the house sit empty would be more costly than
the distortion created by the current structure, which is merely
a transfer from seller to buyer. Thus, giving the intermediary
the full marginal benefit is unlikely to be a superior alternative,
although intermediate systems that capture elements of both
have been proposed (Jares, Larsen, & Zorn, 2000).26
Another possible contractual arrangement would involve
nonlinear commission structures, where the share paid to the
agent would grow in the sales price. However, such a
structure would be difficult to implement, precisely because
the homeowner is less informed than the agent. It is not easy
to see how sellers can set effective breakpoints in a nonlinear
contract if they have imperfect information about home
values and the state of the market. Nonetheless, for price
levels that are well below the minimum threshold of the
home’s value, there is no obvious rationale for paying the
agent a commission, except for the simplicity of the linear
contract. Why, in spite of the obvious flaws, has the current
contractual form in real estate remained so pervasive and
resistant to change, even as parallel improvements in information
have so radically altered the markets and commission
structures for travel agents and stock brokers?
Auctions offer a method for extracting private information
(though in this case the information will more likely
come from potential buyers directly rather than through an
agent). While auctions are becoming more popular as a
method for selling homes, still only a small fraction of
houses are sold this way. This may be because, in part, the
fees involved—which can range from 6% to 10% (Wollam,
2005)—are greater than traditional commissions paid to
agents, eating into any gain due to reduced information
distortions.
Finally, if the information agents provide to home sellers
is an important part of the service they provide, it is
surprising that more sellers don’t more frequently hire an
independent appraiser to inform them of the value of their
home. An appraiser is disinterested in the final transaction
price, which would eliminate the distortions created by the
agent’s contractual form (but could induce moral hazard
problems). Furthermore, appraisers can provide this information
at a fairly low cost. It may be that appraisers, in their
current form, are actually less skilled at valuing homes in a
particular area than are local real estate agents. Nonetheless,
this begs the question why this market niche has not arisen.
Alternatively, it may be the case that agents provide a
bundle of services besides just valuation information, and
these services are worth the commission cost despite the
distortions highlighted above.
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APPENDIX
Observed House Attributes Included in the Analysis
1. Basic Measures of House Scale
Number of bedrooms (categorical): 1, 2, 3, 4, 5, 6ϩ
Number of bathrooms (categorical): 1, 1.5, 2, 2.5, 3, 3.5, 4ϩ
Number of other rooms (categorical): 1, 2, 3, 4, 5, 6, 7ϩ
Number of garage stalls (categorical): 1, 2, 3, 4ϩ
2. Indicators of Housing Quality
Number of fireplaces (categorical): 1, 2, 3ϩ
Presence of (dummy variable equaling 1 if attribute is present in
house): master bath, central air conditioning, other air conditioning
Home age (categorical): unknown, 0–5 years old, 6–10 years old,
11–25 years old, 26–50 years old, 51–100 years old, 100ϩ years old
House exterior style (categorical): American four square, bilevel,
bungalow, Cape Cod, contemporary, colonial, cottage, English,
farmhouse, French provincial, Georgian, prairie, quad-level, Queen
Anne, ranch, step-up ranch, traditional, trilevel, tudor, Victorian,
other
House siding (categorical): aluminum or vinyl, brick, wood, stucco,
other
3. Keywords Used to Describe Home in Listing
(Dummy variable equals 1 if word/phrase or some shortened variant of
it is used in the home description.) needs updating, estate sale,
foreclosure, handyman, as-is, needs TLC, rehabber’s, bank-owned,
priced for a quick sale/priced to sell, motivated, potential, youthful,
close, !, new, spacious, elegance, beautiful, appealing, renovated/
remodeled, vintage, state-of-the-art, maintained, wonderful, brand
new, fantastic, charming, stunning, amazing, granite, immaculate,
breathtaking, neighborhood, spectacular, landscaped, art glass, builtin,
tasteful, must see, fabulous, leaded, delightful, move-in, gourmet,
copper, Corian, custom, unique, maple, newer, hurry, pride,
clean, quiet, dream, block, huge, deck, mint, stately
MARKET DISTORTIONS WHEN AGENTS ARE BETTER INFORMED 611